Back

Explore Courses Blog Tutorials Interview Questions

Explore Tech Questions and Answers

Welcome to Intellipaat Community. Get your technical queries answered by top developers!

0 votes
4 views
by (19k points)

I'm only beginning with keras and machine learning in general.

I trained a model to classify images from 2 classes and saved it using model.save(). Here is the code I used:

from keras.preprocessing.image import ImageDataGenerator

from keras.models import Sequential

from keras.layers import Conv2D, MaxPooling2D

from keras.layers import Activation, Dropout, Flatten, Dense

from keras import backend as K


 

# dimensions of our images.

img_width, img_height = 320, 240

train_data_dir = 'data/train'

validation_data_dir = 'data/validation'

nb_train_samples = 200  #total

nb_validation_samples = 10  # total

epochs = 6

batch_size = 10

if K.image_data_format() == 'channels_first':

    input_shape = (3, img_width, img_height)

else:

    input_shape = (img_width, img_height, 3)

model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape=input_shape))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())

model.add(Dense(64))

model.add(Activation('relu'))

model.add(Dropout(0.5))

model.add(Dense(1))

model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',

              optimizer='rmsprop',

              metrics=['accuracy'])

# this is the augmentation configuration we will use for training

train_datagen = ImageDataGenerator(

    rescale=1. / 255,

    shear_range=0.2,

    zoom_range=0.2,

    horizontal_flip=True)

# this is the augmentation configuration we will use for testing:

# only rescaling

test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(

    train_data_dir,

    target_size=(img_width, img_height),

    batch_size=batch_size,

    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(

    validation_data_dir,

    target_size=(img_width, img_height),

    batch_size=batch_size,

    class_mode='binary')

model.fit_generator(

    train_generator,

    steps_per_epoch=nb_train_samples // batch_size,

    epochs=epochs,

    validation_data=validation_generator,

    validation_steps=5)

model.save('model.h5')

It successfully trained with 0.98 accuracy which is pretty good. To load and test this model on new images, I used the below code:

from keras.models import load_model

import cv2

import numpy as np

model = load_model('model.h5')

model.compile(loss='binary_crossentropy',

              optimizer='rmsprop',

              metrics=['accuracy'])

img = cv2.imread('test.jpg')

img = cv2.resize(img,(320,240))

img = np.reshape(img,[1,320,240,3])

classes = model.predict_classes(img)

print classes

It outputs:

[[0]]

Why wouldn't it give out the actual name of the class and why [[0]]?

Thanks in advance.

1 Answer

0 votes
by (33.1k points)

For your case, if you use Keras predict_classes, that will output a numpy array of class predictions to the index of the neuron of highest activation from your last(softmax) layer. 

Here, [[0]] means that your model predicted that your test data is class 0. If you pass multiple image, and the result would look like [[0], [1], [1], [0]] )

You should convert your actual label (e.g. 'cancer', 'not cancer') into binary encoding (0 for 'cancer', 1 for 'not cancer') for binary classification task. Then you can define your sequence output of [[0]] as having the class label 'cancer'

Hope this answer helps.

Welcome to Intellipaat Community. Get your technical queries answered by top developers!

30.5k questions

32.6k answers

500 comments

108k users

Browse Categories

...